Statistics Papers

Document Type

Journal Article

Date of this Version

2008

Publication Source

Journal of Machine Learning Research

Volume

9

Start Page

2847

Last Page

2880

Abstract

Chain graphs present a broad class of graphical models for description of conditional independence structures, including both Markov networks and Bayesian networks as special cases. In this paper, we propose a computationally feasible method for the structural learning of chain graphs based on the idea of decomposing the learning problem into a set of smaller scale problems on its decomposed subgraphs. The decomposition requires conditional independencies but does not require the separators to be complete subgraphs. Algorithms for both skeleton recovery and complex arrow orientation are presented. Simulations under a variety of settings demonstrate the competitive performance of our method, especially when the underlying graph is sparse.

Keywords

chain graph, conditional independence, decomposition, graphical model, structural learning

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Date Posted: 27 November 2017

This document has been peer reviewed.